Systems and methods for forecasting record object completions

• US 10,657,132 B2
• Filed: 05/23/2019
• Issued: 05/19/2020
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Brunets, Yurii, Jeske, Eric, Dingwall, Nicholas
• Primary Examiner(s): Beausoliel, Jr., Robert W
• Assistant Examiner(s): Rayyan, Susan F
• Application Number:
  US16/421,370
• Publication Number:
  US 20190361890A1
• Time in Patent Office: 362 Days
• CPC Class Codes: G06F 11/3024 where the computing system component is a central processing unit [CPU]
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
• CPC Class Codes: G06F 16/1734 Details of monitoring file system events, e.g. by the use of hooks, filter drivers, logs
• CPC Class Codes: G06F 16/178 Techniques for file synchronisation in file systems
• CPC Class Codes: G06F 16/182 Distributed file systems
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/219 Managing data history or versioning querying versioned data G06F16/2474; querying temporal data G06F16/2477
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2358 Change logging, detection, and notification replication G06F16/27
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2386 : Bulk updating operations data conversion details G06F16/258
• CPC Class Codes: G06F 16/245 : Query processing
• CPC Class Codes: G06F 16/24558 : Binary matching operations
• CPC Class Codes: G06F 16/24564 : Applying rules; Deductive queries
• CPC Class Codes: G06F 16/2457 : with adaptation to user needs
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/24578 : using ranking
• CPC Class Codes: G06F 16/2477 : Temporal data queries
• CPC Class Codes: G06F 16/254 : Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/256 : in federated or virtual databases
• CPC Class Codes: G06F 16/26 : Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/27 : Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/273 : Asynchronous replication or reconciliation
• CPC Class Codes: G06F 16/28 : Databases characterised by their database models, e.g. relational or object models
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/288 : Entity relationship models
• CPC Class Codes: G06F 16/289 : Object oriented databases
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06F 16/313 : Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/337 : Profile generation, learning or modification
• CPC Class Codes: G06F 16/355 : Class or cluster creation or modification
• CPC Class Codes: G06F 16/901 : Indexing; Data structures therefor; Storage structures for retrieval from the web G06F16/951
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90344 : by using string matching techniques
• CPC Class Codes: G06F 16/9035 : Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/906 : Clustering; Classification
• CPC Class Codes: G06F 16/93 : Document management systems
• CPC Class Codes: G06F 16/9535 : Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/6218 : to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6245 : Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 40/20 : Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 40/205 : Parsing
• CPC Class Codes: G06F 40/237 : Lexical tools
• CPC Class Codes: G06F 40/295 : Named entity recognition
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: G06F 7/14 : Merging, i.e. combining at least two sets of record carriers each arranged in the same ordered sequence to produce a single set having the same ordered sequence
• CPC Class Codes: G06F 9/542 : Event management; Broadcasting; Multicasting; Notifications
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/025 : Extracting rules from data
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/02 : using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06Q 10/04 : Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063114 : Status monitoring or status determination for a person or group
• CPC Class Codes: G06Q 10/06312 : Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06315 : Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06393 : Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/06398 : Performance of employee with respect to a job function
• CPC Class Codes: G06Q 10/10 : Office automation; Time management
• CPC Class Codes: G06Q 10/107 : Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 10/109 : Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 10/1091 : Recording time for administrative or management purposes
• CPC Class Codes: G06Q 10/1095 : Meeting or appointment
• CPC Class Codes: G06Q 30/0201 : Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: H04L 12/14 : Charging , metering or billing arrangements for data wireline or wireless communications
• CPC Class Codes: H04L 12/1407 : Policy-and-charging control [PCC] architecture
• CPC Class Codes: H04L 12/1831 : Tracking arrangements for later retrieval, e.g. recording contents, participants activities or behavior, network status
• CPC Class Codes: H04L 2101/00 : Indexing scheme associated with group H04L61/00
• CPC Class Codes: H04L 2101/35 : containing special prefixes
• CPC Class Codes: H04L 2101/37 : E-mail addresses
• CPC Class Codes: H04L 41/082 : the condition being updates or upgrades of network functionality
• CPC Class Codes: H04L 41/14 : Network analysis or design
• CPC Class Codes: H04L 43/026 : using flow identification
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/065 : related to network devices
• CPC Class Codes: H04L 43/067 : using time frame reporting
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0876 : Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/14 : using software, i.e. software packages network security related monitoring H04L63/1408
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/212 : using filtering or selective blocking
• CPC Class Codes: H04L 51/234 : for tracking messages
• CPC Class Codes: H04L 51/42 : Mailbox-related aspects, e.g. synchronisation of mailboxes
• CPC Class Codes: H04L 61/45 : Network directories; Name-to-address mapping
• CPC Class Codes: H04L 61/4594 : Address books, i.e. directories containing contact information about correspondents telephone directories in user terminals H04M1/27453
• CPC Class Codes: H04L 67/125 : involving control of end-device applications over a network
• CPC Class Codes: H04L 67/30 : Profiles
• CPC Class Codes: H04L 67/303 : Terminal profiles
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04M 15/00 : Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
• CPC Class Codes: H04M 15/41 : Billing record details, i.e. parameters, identifiers, structure of call data record [CDR]
• CPC Class Codes: H04M 15/50 : for cross-charging network operators
• CPC Class Codes: H04M 15/58 : based on statistics of usage or network monitoring
• CPC Class Codes: H04M 15/60 : based on actual use of network resources
• CPC Class Codes: H04M 15/66 : Policy and charging system
• CPC Class Codes: H04M 15/755 : Account identification
• CPC Class Codes: H04M 2203/556 : Statistical analysis and interpretation
• CPC Class Codes: H04M 3/2218 : Call detail recording
• CPC Class Codes: H04M 3/436 : Arrangements for screening incoming calls , i.e. evaluating the characteristics of a call before deciding whether to answer it based on the calling party profile H04M3/42059; based on location H04M3/42348; based on presence H04M3/42365; diversion H04M3/54
• CPC Class Codes: H04M 3/5175 : Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/56 : Arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities video conference systems H04N7/15
• CPC Class Codes: H04W 4/24 : Accounting or billing

Abstract

Systems and methods for forecasting record object completions can include accessing, for a first record object, electronic activities linked with the first record object. Each electronic activity can identify participants associated with the first record object. The first record object can include a first object field-value pair identifying a stage of a process. The method can include identifying electronic activities. The systems and methods can include determining a role of participants. The systems and methods can include determining a likelihood that the process of the first record object is completed within a predetermined time period. The systems and methods can include storing an association between the first record object and the likelihood that the process of the first record object is completed within a predetermined time period.

Claims

1. A method comprising:

accessing, by one or more processors, for a first record object of a system of record of a data source provider, data of a plurality of electronic activities transmitted or received via electronic accounts and associated with the first record object, each electronic activity of the plurality of electronic activities identifying one or more participants associated with the first record object, the first record object including a first object field-value pair identifying a stage of a process corresponding to the first record object;

parsing, by the one or more processors, the data of each electronic activity of the plurality of electronic activities associated with the first record object;

determining, by the one or more processors, for a first participant of the one or more participants, a role of the first participant, the role of the first participant determined based on one or more of a seniority of the first participant, a department of the first participant or a second object field-value pair of the first record object identifying an identifier of the first participant as a value of a field of the first record object, the field identifying the role of the first participant;

determining, by the one or more processors, for the first record object, a likelihood that the process of the first record object will be completed within a predetermined time period based on the first object field-value pair identifying the stage of the process corresponding to the first record object, the role of the first participant and the data of each electronic activity of the plurality of electronic activities; and

storing, by the one or more processors, in one or more data structures, an association between the first record object and the likelihood that the process of the first record object will be completed within the predetermined time period.

2. The method of claim 1, further comprising:

identifying, by the one or more processors and for a plurality of second record objects, a set of electronic activities associated with a respective second record object of the plurality of second record objects;

determining, by the one or more processors, a second likelihood that a second process associated with each respective second record object of the plurality of second record objects will be completed within the predetermined time period based on roles of participants of the set of electronic activities associated with the respective second record object of the plurality of second record objects; and

storing, by the one or more processors, in the one or more data structures, a second association between the second likelihood that the second process associated with the each respective second record object of the plurality of second record objects is completed within the predetermined time period.

3. The method of claim 2, further comprising generating a report indicating a metric based on the second likelihood that the second process associated with each respective second record object of the plurality of second record objects is completed within the predetermined time period.

4. The method of claim 1, further comprising:

generating a notification responsive to determining the likelihood that the process of the first record object will be completed within the predetermined time period is below a threshold; and

transmitting the notification to at least one participant of the one or more participants of the plurality of electronic activities associated with the first record object.

5. The method of claim 1, wherein determining the role of the first participant of the one or more participants comprises determining a job title of the first participant.

6. The method of claim 1, further comprising maintaining one or more node profiles for the one or more participants, wherein the one or more node profiles includes a first node profile corresponding to the first participant, the first node profile including at least one of a first node field-value pair identifying a first value of a first field identifying the seniority of the first participant, or a second node field-value pair identifying a second value of a second field identifying the department of the first participant.

7. The method of claim 1, further comprising:

identifying, by the one or more processors, a first set of record objects, wherein each of the first set of record objects are associated with a respective process that was completed by a respective time period;

and wherein determining, for the first record object, the likelihood further comprises determining the likelihood that the process of the first record object will be completed within the predetermined time period using a machine learning model trained with the first set of record objects.

8. The method of claim 7, wherein identifying the first set of record objects further comprises selecting the first set of record objects based on each record object of the first set of record objects having a similarity score with the first record object above a threshold.

9. The method of claim 1, further comprising generating a report indicating a metric based on the likelihood that the process of the first record object will be completed within the predetermined time period.

10. The method of claim 1, further comprising:

identifying, by the one or more processors, a new match between a second electronic activity and the first record object;

identifying, by the one or more processors, a second participant of the electronic activity different than the one or more participants; and

updating, by the one or more processors, the likelihood that the process of the first record object will be completed within the predetermined time period based on a second role of the second participant.

11. The method of claim 1, wherein determining the likelihood includes determining the likelihood that the process of the first record object will be completed within the predetermined time period based on an engagement score of the one or more participants, the engagement score based on a volume of electronic activities transmitted or received by the one or more participants.

12. A system comprising:

one or more processors configured by machine-readable instructions to;

access for a first record object of a system of record of a data source provider, data of a plurality of electronic activities transmitted or received via electronic accounts and associated with the first record object, each electronic activity of the plurality of electronic activities identifying one or more participants associated with the first record object, the first record object including a first object field-value pair identifying a stage of a process corresponding to the first record object;

parse the data of each electronic activity of the plurality of electronic activities associated with the first record object;

determine, for a first participant of the one or more participants, a role of the first participant, the role of the first participant determined based on one or more of a seniority of the first participant, a department of the first participant or a second object field-value pair of the first record object identifying an identifier of the first participant as a value of a field of the first record object, the field identifying the role of the first participant;

determine for the first record object, a likelihood that the process of the first record object will be completed within a predetermined time period based on the first object field-value pair identifying the stage of the process corresponding to the first record object, the role of the first participant and the data of each electronic activity of the plurality of electronic activities; and

store in one or more data structures, an association between the first record object and the likelihood that the process of the first record object will be completed within the predetermined time period.

13. The system of claim 12, wherein the one or more processors are further configured by machine-readable instructions to:

identify, for a plurality of second record objects, a set of electronic activities associated with a respective second record object of the plurality of second record objects;

determine a second likelihood that a second process associated with each respective second record object of the plurality of second record objects will be completed within the predetermined time period based on roles of participants of the set of electronic activities associated with the respective second record object of the plurality of second record objects; and

store in the one or more data structures, a second association between the second likelihood that the second process associated with the each respective second record object of the plurality of second record objects is completed within the predetermined time period.

14. The system of claim 13, wherein the one or more processors are further configured by machine-readable instructions to:

generate a report indicating a metric based on the second likelihood that the second process associated with each respective second record object of the plurality of second record objects is completed within the predetermined time period.

15. The system of claim 12, wherein the one or more processors are further configured by machine-readable instructions to:

generate a notification responsive to determining the likelihood that the process of the first record object will be completed within the predetermined time period is below a threshold; and

transmit the notification to at least one participant of the one or more participants of the plurality of electronic activities associated with the first record object.

16. The system of claim 12, wherein to determine the role of the first participant, the one or more processors are configured by machine-readable instructions to determine a job title of the first participant.

17. The system of claim 12, wherein the one or more processors are further configured by machine-readable instructions to maintain one or more node profiles for the one or more participants, wherein the one or more node profiles includes a first node profile corresponding to the first participant, the first node profile including at least one of a first node field-value pair identifying a first value of a first field identifying the seniority of the first participant, or a second node field-value pair identifying a second value of a second field identifying the department of the first participant.

18. The system of claim 12, wherein the one or more processors are further configured by machine-readable instructions to identify a first set of record objects, wherein each of the first set of record objects are associated with a respective process that was completed by a respective time period;

and wherein to determine the likelihood, the one or more processors are configured by machine-readable instructions to determine the likelihood that the process of the first record object will be completed within the predetermined time period using a machine learning model trained with the first set of record objects.

19. The system of claim 18, wherein to identify the first set of record objects, the one or more processors are configured by machine-readable instructions to select the first set of record objects based on each record object of the first set of record objects having a similarity score with the first record object above a threshold.

20. A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to cause the one or more processors to:

access for a first record object of a system of record of a data source provider, data of a plurality of electronic activities transmitted or received via electronic accounts and associated with the first record object, each electronic activity of the plurality of electronic activities identifying one or more participants associated with the first record object, the first record object including a first object field-value pair identifying a stage of a process corresponding to the first record object;

parse the data of each electronic activity of the plurality of electronic activities associated with the first record object;

determine, for a first participant of the one or more participants, a role of the first participant, the role of the first participant determined based on one or more of a seniority of the first participant, a department of the first participant or a second object field-value pair of the first record object identifying an identifier of the first participant as a value of a field of the first record object, the field identifying the role of the first participant;

determine for the first record object, a likelihood that the process of the first record object will be completed within a predetermined time period based on the first object field-value pair identifying the stage of the process corresponding to the first record object, the role of the first participant and the data of each electronic activity of the plurality of electronic activities; and

store in one or more data structures, an association between the first record object and the likelihood that the process of the first record object will be completed within the predetermined time period.